8
regional and macro-regional environmental governance (e.g. at the scale of the
entire Baltic Sea) are less prevalent in the scientifi c literature on environmental
governance than both at the local and global levels (e.g. Balsiger and Debarbieux
2011 ; Gilek and Kern 2015 ), which underlines the need for the regional perspective
explored in this volume.
1.2.2 Assessment: Management Processes and Interactions
The interactions between the primarily science-based assessment sphere (i.e.
generation of knowledge on environmental status, pressures, risks and problems)
and the management sphere (i.e. decisions on and implementation of actions) have
been described as key processes in environmental governance (Renn et al. 2011 ;
Rice 2005 ). Science has since long been seen as the primary provider of knowledge
and advice to guide environmental policy-making, especially in the case of managing
environmental risks stemming from industrial technologies and pollutants (Karlsson
et al. 2011 ). This has also been the case in the Baltic Sea region, both nationally and
in relation to the activities of international organisations such as HELCOM and
ICES (Udovyk and Gilek 2013 ).
However, interactions over science-policy interfaces (e.g. connected with the
evaluation of what constitutes good environmental status and unacceptable levels of
risk) are usually complicated by severe challenges connected with complexity ,
ignorance , uncertainty and ambiguity (Renn 2008 ; Stirling 2007 ), which frequently
result in controversy in both society and science on appropriate risk assessment and
management. It has been argued that scientifi c uncertainties and stakeholder
disagreements and confl icts are particularly problematic for marine environmental
governance when implementing holistic management approaches such as EAM and
MSP (Linke et al. 2014 ; Rice 2005 ; Wilson 2009 ). Observations of impaired public
trust in science and recognition of other legitimate knowledge providers, such as
practitioners, stakeholders and experts based elsewhere than in traditional research
organisations, have also been linked to cases of severe scientifi c uncertainty (Irwin
and Michael 2003 ), in combination with a common politicisation of science (e.g.
Eriksson et al. 2010 ; Weingart 1999 ). In response, Stirling ( 2007 )
2 has argued that
different types of environmental issues characterised by uncertainty and ambiguity
require an expansion of traditional strategies in science and policy, to include
precautionary and participative approaches.
As a consequence, the relationship between science and policy is changing on
both a theoretical and practical level, particularly with regard to complex
environmental issues such as marine governance. It is, however, despite a long and
2 Stirling ( 2007 ) differentiates between four types of scientifi c incertitude: risk (quantitative data
and knowledge exist), uncertainty (qualitative understanding of outcome, but not probabilities),
ambiguity (poor knowledge about potential outcome) and ignorance; see Linke et al. ( 2016 ) for
further explanation.
M. Gilek et al.
regional and macro-regional environmental governance (e.g. at the scale of the
entire Baltic Sea) are less prevalent in the scientifi c literature on environmental
governance than both at the local and global levels (e.g. Balsiger and Debarbieux
2011 ; Gilek and Kern 2015 ), which underlines the need for the regional perspective
explored in this volume.
1.2.2 Assessment: Management Processes and Interactions
The interactions between the primarily science-based assessment sphere (i.e.
generation of knowledge on environmental status, pressures, risks and problems)
and the management sphere (i.e. decisions on and implementation of actions) have
been described as key processes in environmental governance (Renn et al. 2011 ;
Rice 2005 ). Science has since long been seen as the primary provider of knowledge
and advice to guide environmental policy-making, especially in the case of managing
environmental risks stemming from industrial technologies and pollutants (Karlsson
et al. 2011 ). This has also been the case in the Baltic Sea region, both nationally and
in relation to the activities of international organisations such as HELCOM and
ICES (Udovyk and Gilek 2013 ).
However, interactions over science-policy interfaces (e.g. connected with the
evaluation of what constitutes good environmental status and unacceptable levels of
risk) are usually complicated by severe challenges connected with complexity ,
ignorance , uncertainty and ambiguity (Renn 2008 ; Stirling 2007 ), which frequently
result in controversy in both society and science on appropriate risk assessment and
management. It has been argued that scientifi c uncertainties and stakeholder
disagreements and confl icts are particularly problematic for marine environmental
governance when implementing holistic management approaches such as EAM and
MSP (Linke et al. 2014 ; Rice 2005 ; Wilson 2009 ). Observations of impaired public
trust in science and recognition of other legitimate knowledge providers, such as
practitioners, stakeholders and experts based elsewhere than in traditional research
organisations, have also been linked to cases of severe scientifi c uncertainty (Irwin
and Michael 2003 ), in combination with a common politicisation of science (e.g.
Eriksson et al. 2010 ; Weingart 1999 ). In response, Stirling ( 2007 )
2 has argued that
different types of environmental issues characterised by uncertainty and ambiguity
require an expansion of traditional strategies in science and policy, to include
precautionary and participative approaches.
As a consequence, the relationship between science and policy is changing on
both a theoretical and practical level, particularly with regard to complex
environmental issues such as marine governance. It is, however, despite a long and
2 Stirling ( 2007 ) differentiates between four types of scientifi c incertitude: risk (quantitative data
and knowledge exist), uncertainty (qualitative understanding of outcome, but not probabilities),
ambiguity (poor knowledge about potential outcome) and ignorance; see Linke et al. ( 2016 ) for
further explanation.
M. Gilek et al.